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Image-Based Leopard Seal Recognition: Approaches and Challenges in Current Automated Systems

Jorge Yero Salazar, Pablo Rivas, Renato Borras-Chavez, Sarah Kienle

TL;DR

This work addresses the problem of recognizing leopard seals from conventional wildlife photography in harsh, remote habitats where data collection is labor-intensive. It surveys detection and segmentation methods, contrasting manual landmark approaches with automated detectors and analyzes the potential of Vision Transformers and the Segment Anything Model for marine wildlife imaging. The paper synthesizes SOTA methods for conventional imagery of pinnipeds, discusses key challenges, and highlights ViT and SAM as promising avenues for robust, scalable seal recognition. Ultimately, it offers guidance for developing non-intrusive, efficient monitoring tools that can aid conservation and ecosystem health assessment in Antarctic environments.

Abstract

This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learning technologies. We used the leopard seal, \emph{Hydrurga leptonyx}, a key species within Antarctic ecosystems, to review the different available methods found. As apex predators, Leopard seals are characterized by their significant ecological role and elusive nature so studying them is crucial to understand the health of their ecosystem. Traditional methods of monitoring seal species are often constrained by the labor-intensive and time-consuming processes required for collecting data, compounded by the limited insights these methods provide. The advent of machine learning, particularly through the application of vision transformers, heralds a new era of efficiency and precision in species monitoring. By leveraging state-of-the-art approaches in detection, segmentation, and recognition within digital imaging, this paper presents a synthesis of the current landscape, highlighting both the cutting-edge methodologies and the predominant challenges faced in accurately identifying seals through photographic data.

Image-Based Leopard Seal Recognition: Approaches and Challenges in Current Automated Systems

TL;DR

This work addresses the problem of recognizing leopard seals from conventional wildlife photography in harsh, remote habitats where data collection is labor-intensive. It surveys detection and segmentation methods, contrasting manual landmark approaches with automated detectors and analyzes the potential of Vision Transformers and the Segment Anything Model for marine wildlife imaging. The paper synthesizes SOTA methods for conventional imagery of pinnipeds, discusses key challenges, and highlights ViT and SAM as promising avenues for robust, scalable seal recognition. Ultimately, it offers guidance for developing non-intrusive, efficient monitoring tools that can aid conservation and ecosystem health assessment in Antarctic environments.

Abstract

This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learning technologies. We used the leopard seal, \emph{Hydrurga leptonyx}, a key species within Antarctic ecosystems, to review the different available methods found. As apex predators, Leopard seals are characterized by their significant ecological role and elusive nature so studying them is crucial to understand the health of their ecosystem. Traditional methods of monitoring seal species are often constrained by the labor-intensive and time-consuming processes required for collecting data, compounded by the limited insights these methods provide. The advent of machine learning, particularly through the application of vision transformers, heralds a new era of efficiency and precision in species monitoring. By leveraging state-of-the-art approaches in detection, segmentation, and recognition within digital imaging, this paper presents a synthesis of the current landscape, highlighting both the cutting-edge methodologies and the predominant challenges faced in accurately identifying seals through photographic data.
Paper Structure (15 sections, 2 equations, 6 figures, 1 table)

This paper contains 15 sections, 2 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Approaches for detection and segmentation of seals using conventional photographs
  3. Seals detection
  4. Seal detection on conventional photographs
  5. Other species detection on conventional photographs
  6. Comparing approaches and identifying challenges
  7. Seals segmentation
  8. Seal segmentation on conventional photographs
  9. Segment Anything Model
  10. Comparing approaches and identifying challenges
  11. Vision transformer's popularity in the SOTA of image recognition. Potential application for seal recognition in marine environments
  12. Vision Transformer; the popular option
  13. Potential of ViT for recognizing/identifying seals in surface marine environments
  14. Challenges in seal recognition from the perspectives of Computer Vision and Machine Learning. Impact over the development and performance of automated detection systems
  15. Conclusion

Figures (6)

  • Figure 1: Methodology for segmentation following the work in seal_segmentation_zhelezniakov2015segmentation. The image was created based on their image methodology but kept only the part aligned with our question.
  • Figure 2: Segment Anything Model (SAM) overview. Image was based on the architecture figure from segment_anything_kirillov2023segment using our database.
  • Figure 3: Seal image segmentation using SAM segment_anything_kirillov2023segment.
  • Figure 4: Samples of leopard seals on different body sides.
  • Figure 5: Samples that represent different data challenges.
  • ...and 1 more figures